Multilingually Trained Bottleneck Features in Spoken Language Recognition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F17%3APU126448" target="_blank" >RIV/00216305:26230/17:PU126448 - isvavai.cz</a>
Result on the web
<a href="http://www.sciencedirect.com/science/article/pii/S0885230816302947" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0885230816302947</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.csl.2017.06.008" target="_blank" >10.1016/j.csl.2017.06.008</a>
Alternative languages
Result language
angličtina
Original language name
Multilingually Trained Bottleneck Features in Spoken Language Recognition
Original language description
Multilingual training of neural networks has proven to be simple yet effective way to deal with multilingual training corpora. It allows to use several resources to jointly train a language independent representation of features, which can be encoded into low-dimensional feature set by embedding narrow bottleneck layer to the network. In this paper, we analyze such features on the task of spoken language recognition (SLR), focusing on practical aspects of training bottleneck networks and analyzing their integration in SLR. By comparing properties of mono and multilingual features we show the suitability of multilingual training for SLR. The state-of-the-art performance of these features is demonstrated on the NIST LRE09 database.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
COMPUTER SPEECH AND LANGUAGE
ISSN
0885-2308
e-ISSN
1095-8363
Volume of the periodical
2017
Issue of the periodical within the volume
46
Country of publishing house
GB - UNITED KINGDOM
Number of pages
16
Pages from-to
252-267
UT code for WoS article
000407609600015
EID of the result in the Scopus database
2-s2.0-85023638945